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Found 16 papers, 11 papers with code
ORLA*: Mobile Manipulator-Based Object Rearrangement with Lazy A*
Effectively performing object rearrangement is an essential skill for mobile manipulators, e. g., setting up a dinner table or organizing a desk.
Few-shot Class-incremental Pill Recognition
In practice, the expensive cost of data annotation and the continuously increasing categories of new pills make it meaningful to develop a few-shot class-incremental pill recognition system.
class-incremental learning
Few-Shot Class-Incremental Learning
+3
USNID: A Framework for Unsupervised and Semi-supervised New Intent Discovery
New intent discovery is of great value to natural language processing, allowing for a better understanding of user needs and providing friendly services.
A Self-Adjusting Fusion Representation Learning Model for Unaligned Text-Audio Sequences
In this paper, a Self-Adjusting Fusion Representation Learning Model (SA-FRLM) is proposed to learn robust crossmodal fusion representations directly from the unaligned text and audio sequences.
Make Acoustic and Visual Cues Matter: CH-SIMS v2.0 Dataset and AV-Mixup Consistent Module
The designed modality mixup module can be regarded as an augmentation, which mixes the acoustic and visual modalities from different videos.
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation
However, it is a great challenge to learn a new domain incrementally without catastrophically forgetting previous knowledge.
Toward Efficient Task Planning for Dual-Arm Tabletop Object Rearrangement
We investigate the problem of coordinating two robot arms to solve non-monotone tabletop multi-object rearrangement tasks.
Deep Contrastive One-Class Time Series Anomaly Detection
To overcome the shortcomings, a deep Contrastive One-Class Anomaly detection method of time series (COCA) is proposed by authors, following the normality assumptions of CL and one-class classification.
M-SENA: An Integrated Platform for Multimodal Sentiment Analysis
The platform features a fully modular video sentiment analysis framework consisting of data management, feature extraction, model training, and result analysis modules.
Lazy Rearrangement Planning in Confined Spaces
Object rearrangement is important for many applications but remains challenging, especially in confined spaces, such as shelves, where objects cannot be accessed from above and they block reachability to each other.
Consistent Representation Learning for Continual Relation Extraction
Specifically, supervised contrastive learning based on a memory bank is first used to train each new task so that the model can effectively learn the relation representation.
TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition
It is composed of two main modules: open intent detection and open intent discovery.
Representation Iterative Fusion based on Heterogeneous Graph Neural Network for Joint Entity and Relation Extraction
Joint entity and relation extraction is an essential task in information extraction, which aims to extract all relational triples from unstructured text.
Ranked #2 on
Relation Extraction
on SemEval-2010 Task 8
Joint Entity and Relation Extraction
Relation Classification
A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation
Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN.
Uniform Object Rearrangement: From Complete Monotone Primitives to Efficient Non-Monotone Informed Search
DFSDP is extended to solve single-buffer, non-monotone instances, given a choice of an object and a buffer.
Cross-Modal BERT for Text-Audio Sentiment Analysis
In this paper, we propose the Cross-Modal BERT (CM-BERT), which relies on the interaction of text and audio modality to fine-tune the pre-trained BERT model.
Ranked #3 on
Multimodal Sentiment Analysis
on MOSI
